diff options
Diffstat (limited to 'python/exploration')
| -rw-r--r-- | python/exploration/curve_trades.py | 302 |
1 files changed, 0 insertions, 302 deletions
diff --git a/python/exploration/curve_trades.py b/python/exploration/curve_trades.py deleted file mode 100644 index d17b35ed..00000000 --- a/python/exploration/curve_trades.py +++ /dev/null @@ -1,302 +0,0 @@ -from analytics.index_data import get_index_quotes, index_returns -from db import dbengine -from analytics import Index, Portfolio -from analytics.utils import roll_date -from dateutil.relativedelta import relativedelta -import pandas as pd -import math -import statsmodels.formula.api as smf -from analytics.basket_index import MarkitBasketIndex -import numpy as np -import matplotlib.pyplot as plt - -from statsmodels.sandbox.regression.predstd import wls_prediction_std -from scipy.interpolate import interp1d - -serenitasdb = dbengine('serenitasdb') -dawndb = dbengine('dawndb') - -def on_the_run(index): - r = serenitasdb.execute("SELECT max(series) FROM index_version WHERE index=%s", - (index,)) - series, = r.fetchone() - return series - -def curve_spread_diff(index='IG', rolling=6, years=3, percentage=False, percentage_base='5yr'): - otr = on_the_run(index) - ## look at spreads - df = get_index_quotes(index, list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr'], years=years) - spreads = df.groupby(level=['date', 'tenor']).nth(-1)['closespread'].unstack(-1) - spreads_diff = spreads.diff(axis=1) - del spreads_diff['3yr'] - spreads_diff.columns = ['3-5', '5-7', '7-10'] - spreads_diff['5-10'] = spreads_diff['5-7'] + spreads_diff['7-10'] - if percentage is True: - spreads_diff = spreads.apply(lambda df: df/df[percentage_base], axis = 1) - return spreads_diff - -def spreads_diff_table(spreads_diff): - def current(s): - return s.iat[-1] - def zscore(s): - return (s.iat[-1] - s.mean()) / s.std() - df = spreads_diff.agg(['min', 'max','mean', current, zscore]) - ((spreads_diff - spreads_diff.mean())/spreads_diff.std()).plot() - return df - -def theta_matrix_by_series(index='IG', rolling=6): - otr = on_the_run(index) - df = get_index_quotes(index, list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']) - df['theta_per_dur'] = df.theta2 / df.duration2 - theta_matrix = df.groupby(level=['date', 'tenor','series']).nth(-1)['theta_per_dur'] - theta_matrix = theta_matrix.loc[theta_matrix.index[-1][0]].unstack(0) - return theta_matrix[['3yr', '5yr', '7yr', '10yr']] - -def ratio_within_series(index='IG', rolling=6, param='duration'): - otr = on_the_run(index) - df = get_index_quotes(index, list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']).unstack() - ratio = (df[param]. - apply(lambda s: s / df[param]['5yr'].values, raw=True)) - ratio.columns = pd.MultiIndex.from_product([[param + '_ratio_to_5yr'], ratio.columns]) - df = df.join(ratio).groupby(['date']).tail(1) - df = df.reset_index(level=['index', 'version'], drop=True) - return df - -def curve_3_5_10(df): - """ - Parameters - ---------- - df: duration ratio within series""" - #buy 3y, sell 5y, buy 10y - s = - df.theta2['3yr'] / df.duration_ratio_to_5yr['3yr'] \ - + 2 * df.theta2['5yr'] \ - - df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr'] - s.dropna().unstack(-1).plot() - -def curve_5_10(df): - #buy sell 5y, buy 10y - s = df.theta2['5yr'] - df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr'] - s.dropna().unstack(-1).plot() - -def on_the_run_theta(index='IG', rolling=6): - otr = on_the_run(index) - df = get_index_quotes(index, list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']) - df['theta_per_dur'] = df.theta2/df.duration2 - theta_matrix = df.groupby(level=['date', 'tenor']).nth(-1)['theta_per_dur'] - theta_matrix.unstack(-1).plot() - -def curve_returns(index='IG', rolling=6): - ## look at returns - otr = on_the_run(index) - df = index_returns(index=index, series=list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']) - ## on-the-run returns - df = df.reset_index().set_index(['date', 'series', 'tenor']) - returns = df.price_return.dropna().unstack(-1).groupby(level='date').nth(-1) - - strategies_return = pd.DataFrame( - {'5-10': 1.78 * returns['5yr'] - returns['10yr'], - '7-10': 1.33 * returns['7yr'] - returns['10yr'], - '3-5-10': -2 * returns['3yr'] + 3 * returns['5yr'] - returns['10yr'], - '3-5': returns['5yr'] - 1.56 * returns['3yr'], - '3-7': returns['7yr'] - 2.07 * returns['3yr']}) - strategies_return_monthly = (strategies_return. - groupby(pd.Grouper(freq='M')). - agg(lambda df: (1 + df).prod() - 1)) - - def sharpe(df, period="daily"): - if period == "daily": - return df.mean() / df.std() * math.sqrt(252) - else: - return df.mean() / df.std() * math.sqrt(12) - - results = strategies_return.agg([sharpe, lambda df: df.nsmallest(10).mean()]) - sharpe_monthly = strategies_return_monthly.agg(sharpe, period="monthly") - sharpe_monthly.name = 'Monthly Sharpe' - results.index=['Sharpe', 'Mean Worst 10 Days DrawDown'] - return results.append(sharpe_monthly) - -def cross_series_curve(index='IG', rolling=6): - otr = on_the_run(index) - df = index_returns(index= index, series=list(range(otr - rolling, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr']) - ## look cross series - 3y to 5y - df = df.reset_index().set_index(['date', 'index', 'tenor', 'series']) - returns1 = df.xs(['5yr', index], level = ['tenor','index']).price_return.unstack(-1) - price_diff = pd.DataFrame() - for ind in list(range(otr - 2, otr + 1)): - price_diff[ind] = returns1[ind] - 1.6 * returns1[ind - 4] - - price_diff = price_diff.stack().groupby(level = 'date').nth(-1) - monthly_returns_cross_series = (price_diff. - groupby(pd.Grouper(freq='M')). - agg(lambda df: (1 + df).prod() - 1)) - plt.plot(monthly_returns_cross_series) - -def forward_loss(index='IG'): - start_date = (pd.Timestamp.now() - pd.DateOffset(years=3)).date() - - df = pd.read_sql_query("SELECT date, index, series, tenor, duration, closespread, "\ - "closespread*duration / 100 AS indexel " \ - "FROM index_quotes WHERE index=%s AND date >= %s " \ - "ORDER BY date DESC, series ASC, duration ASC", - serenitasdb, parse_dates=['date'], params=[index, start_date]) - df1 = pd.read_sql_query("SELECT index, series, tenor, maturity FROM index_maturity", - serenitasdb, parse_dates=['maturity']) - - df = df.merge(df1, on=['index','series','tenor']) - df = df.set_index(['date','index', 'maturity']).dropna() - df = df.groupby(level=['date','index', 'maturity']).nth(-1) - # annual change, to take out some noise - df['fwd_loss_rate'] = df.indexel.diff(2)/df.duration.diff(2) - -def curve_model(tenor_1='5yr', tenor_2='10yr'): - #OLS model - df = ratio_within_series(param='closespread') - df = pd.concat([df.duration[tenor_1], df.duration[tenor_2], - df.closespread[tenor_1], - df.closespread_ratio_to_5yr[tenor_2], - df.theta[tenor_1], df.theta[tenor_2]], - axis=1, - keys=['duration1', 'duration2', 'closespread', - 'ratio', 'theta1', 'theta2']) - df = np.log(df) - ols_model = smf.ols('ratio ~ closespread + duration1 + theta1 + theta2', - data=df).fit() - return df, ols_model - -def curve_model_results(df, model): - df = df.dropna() - prstd_ols, df['down_2_stdev'], df['up_2_stdev'] = wls_prediction_std(model) - #dr/dspread = exp(k) + spread_coeff * duration ^ dur_coeff * spread ^ (spread_coeff-1) - cols = ['ratio', 'closespread', 'down_2_stdev', 'up_2_stdev'] - df[cols] = np.exp(df[cols]) - df['predicted'] = np.exp(model.predict()) - df[['predicted', 'down_2_stdev', 'up_2_stdev']]=\ - df[['predicted', 'down_2_stdev', 'up_2_stdev']].multiply(df['closespread'].values, axis=0) - ax = df[['predicted', 'down_2_stdev', 'up_2_stdev']].reset_index(level='series', drop=True).plot() - df['dr_dspread'] = np.exp(model.params[0]) * model.params[2] * df.duration1 ** model.params[1] * df.closespread ** (model.params[2] - 1) - return df - -def spread_fin_crisis(index='IG'): - otr = on_the_run(index) - ## look at spreads - df = get_index_quotes(index, list(range(8, otr + 1)), - tenor=['3yr', '5yr', '7yr', '10yr'], years=20) - spreads = df.groupby(level=['date', 'tenor']).nth(-1)['closespread'].unstack(-1) - spreads_diff = spreads.diff(axis=1) - to_plot = pd.DataFrame() - to_plot['spread'] = spreads['5yr'] - to_plot['3 - 5 diff'] = spreads_diff['5yr'] - to_plot['5 - 10 diff'] = spreads_diff['7yr'] + spreads_diff['10yr'] - - fig = plt.figure() - ax = fig.add_subplot(111) - ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax - - width = 0.4 - to_plot['spread'].plot(color='red', ax=ax) - to_plot['5 - 10 diff'].plot(color='blue', ax=ax2) - to_plot['3 - 5 diff'].plot(color='green', ax=ax2) - plt.legend(bbox_to_anchor=(.5, -.1), ncol = 2) - - plt.show() - -def forward_spread(index='IG', series=None, tenors=['3yr', '5yr', '7yr', '10yr']): - - if series is None: - series = on_the_run(index = index) - b_index = MarkitBasketIndex(index, series, tenors) - b_index.tweak() - - f_spread = [] - date_range = pd.bdate_range(pd.datetime.today(), max(b_index.maturities), freq='M') - for d in date_range.date: - b_index.trade_date = d - f_spread.append(b_index.spread()) - return pd.concat(f_spread, keys=date_range).unstack(-1) - -def spot_forward(index='IG', series=None, tenors=['3yr', '5yr', '7yr', '10yr']): - - if series is None: - series = on_the_run(index) - b_index = MarkitBasketIndex(index, series, tenors) - b_index.tweak() - - spreads_current = b_index.spread() - spreads_current.name = 'current' - spreads_1yr = pd.Series([b_index.spread(m - relativedelta(years=1), b_index.coupon(m)) \ - for m in b_index.maturities], index=tenors) - spreads_1yr.name = '1yr' - df = pd.concat([spreads_current, spreads_1yr], axis=1) - maturity_1yr = roll_date(b_index.index_desc.issue_date[0], 1) - df_0 = pd.DataFrame({'current':[0., b_index.spread(maturity_1yr, - 0.01 if index == "IG" else 0.05)], - '1yr': [0., 0.]}, index=['0yr', '1yr']) - df_0.index.name = 'tenor' - df = df_0.append(df) - df['maturity'] = [b_index.trade_date, maturity_1yr] + b_index.maturities - return df.reset_index().set_index('maturity') - -def curve_pos(trade_date, index='IG'): - - ''' - trade_date : :class:`datetime.date` - index : string - one of 'IG', 'HY' or 'ITRX' - - Returns a Portfolio of curve trades ''' - - sql_string = "SELECT * FROM cds where trade_date < %s" - df = pd.read_sql_query(sql_string, dawndb, parse_dates=['trade_date', 'maturity'], - params=[trade_date]) - df = df[df['folder'] == f'SER_{index}CURVE'] - df.notional = df.notional.where(df.protection == 'Seller', -df.notional) - df = df.groupby(['security_id', 'maturity'])['notional'].sum() - df = df.iloc[df.nonzero()[0]].reset_index() - - sql_string = "SELECT * FROM index_maturity LEFT JOIN index_version USING (index, series)" - lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity']) - - df = df.merge(lookup_table, left_on=['security_id','maturity'], right_on=['redindexcode', 'maturity']) - - indices = [] - sql_string = "SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s" - for i, row in df[['index', 'tenor', 'series', 'notional']].iterrows(): - temp = Index.from_name(row['index'], row.series, row.tenor) - temp.value_date = trade_date - if row.notional > 0: - temp.direction = 'Seller' - temp.notional = abs(row.notional) - spread_df = pd.read_sql_query(sql_string, serenitasdb, - params=[row['index'], row.series, row.tenor, trade_date]) - temp.spread = spread_df.iloc[0][0] - indices.append(temp) - - return Portfolio(indices) - -def curve_shape(trade_date, index='IG', percentile=.95): - - ''' - Returns a function to linearly interpolate between the curve based on maturity (in years)''' - - curve_shape = curve_spread_diff(index, 10, 5, True) - steepness = (curve_shape['10yr']/curve_shape['3yr']) - series = on_the_run(index) - - sql_string = "SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s" - spread_df = pd.read_sql_query(sql_string, serenitasdb, - params=[index, series, '5yr', trade_date.date()]) - sql_string = "SELECT tenor, maturity FROM index_maturity where index = %s and series = %s" - lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity'], params=[index, series]) - - df = curve_shape[steepness == steepness.quantile(percentile, 'nearest')] - df = df * spread_df.iloc[0][0]/df['5yr'][0] - df = df.stack().rename('spread') - df = df.reset_index().merge(lookup_table, on=['tenor']) - df['year_frac'] = (df.maturity - pd.to_datetime(trade_date)).dt.days/365 - return interp1d(np.hstack([0, df.year_frac]), np.hstack([0, df.spread])) |